For me specifically, StatsModels, sklearn, REPL plotting (more of a language feature) with seaborn/matplotlib, scipy, various NLP libraries, pandas for parsing CSVs and fast vector operations on table-like data, an easy to use requests library (compared to http://golang.org/pkg/net/http/) and 10^6 C libraries that either someone already made bindings for or that you can easily make bindings for with Swig/boost (not sure if it's as easy with Go).
Not to mention, all the "convenience" libs that are easy to implement but would take forever to make them all yourself. Examples like jaro distance, jaro-winkel distance, etc.
Unrelatedly, I wish Nim was more popular and I wish they concentrated on math more. Having a general purpose fast language that you can prototype in is amazing. Although you can't really blame such a small community for not focusing on a niche.
Julia might be great eventually, and it's really fun to write in and much easier to implement new things in compared to Cython, but not so great right now for my purposes. You end up importing the Julia library from Python and it takes a month to JIT compile some things. Also, string processing is not nearly as easy as it is in Python.
The problem for scientific users seems to be to achieve enough consensus about which language to use as an "obvious choice" that a bunch of other scientific users make packages.
Since we are talking about "fast" languages like Nim and Julia, I wonder why R and Python have done so well with the scientific audience but C++, Java, C# and the like never developed anything quite comparable. Or did they and I just didn't notice?
